Analysis of COVID-19 Misinformation in Social Media using Transfer Learning
Bibliographic record
Abstract
Most major events are often accompanied by misinformation on online Social Networking platforms. Due to its nature, the COVID-19 pandemic was bound to lead to an explosion of information online, much of it false or misleading. This information explosion, termed "infodemic" by the World Health Organization (WHO), has revealed the need for automatic fake news detection to help with the exponentially growing flow of unverified information. The objective of this study is to explore combinations of different supervised classification models trained on different general and domain-specific embeddings, and compare the effects of the iterations on the results. We also analyze the results to determine whether the differences in weighted F1-score performance metrics are statistically significant. Ultimately, we demonstrate that concatenation of general and context-specific embeddings improves performance. Our research shows promise for health misinformation detection and formulation of effective public health responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".